Goekdeniz-Guelmez/mlx-lm-lora
Train Large Language Models on MLX.
Star History
Momentum
+10
STARS · LAST 30 DAYS
1
PER DAY
#463
MOST-STARRED Python
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +7 | +10 | +90 |
| Per day | 1 | 1 | 1 |
| Forks gained | +1 | +3 | +10 |
mlx-lm-lora gained 10 stars in the last 30 days, about 1 a day, and now has 419. It is about 1 year old and has averaged roughly 419 stars a year. It ranks #463 among Python repositories and #3,900 across all languages on GitHubRepo.
Trending Record
1
DAYS ON TRENDING
#3711
BEST RANK
Sep 28, 2026
FIRST APPEARANCE
Active
STATUS TODAY
mlx-lm-lora has maintained a continuous presence across global trending indexes, peaking at #3711. Below is the 30-day activity profile:
💡 Overview
mlx-lm-lora is an open-source project written in Python: Train Large Language Models on MLX.
Engineered for speed, consistency, and developer ease, it solves common hurdles in apple, deep-learning, dpo. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Python for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ pip install mlx-lm-lora⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Python >= 3.9, pip, virtualenv
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
mlx-lm-lora coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Autonomous AI Agents
Orchestrate intelligent workflows and tool-calling routines with mlx-lm-lora.
Model Inference & Prompting
Integrate fast, local or cloud-hosted generative AI models directly into production code.
Context Memory & RAG
Augment language models with dynamic vector retrieval and structured project memory.
Developer Productivity
Automate repetitive engineering tasks, code generation, and test creation using AI agents.
🚀 Getting Started
Install mlx-lm-lora using your package manager: `pip install mlx-lm-lora`
Initialize your project workspace or configuration file for mlx-lm-lora.
Import mlx-lm-lora into your codebase or invoke it directly from your terminal.
Execute your test suite or run `mlx-lm-lora --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
Goekdeniz-Guelmez/mlx-lm-lora is currently ranked #3,900 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
| Rank | Repository | Language | Stars | Action |
|---|---|---|---|---|
| #3,895 | fastladder/fastladder | JavaScript | ★ 420 | Compare ↗ |
| #3,895 | vgvassilev/clad | C++ | ★ 420 | Compare ↗ |
| #3,895 | WFCD/warframe-items | TypeScript | ★ 420 | Compare ↗ |
| #3,895 | creepymonster/GlucoseDirect | Swift | ★ 420 | Compare ↗ |
| #3,895 | Snd-R/Komelia | Kotlin | ★ 420 | Compare ↗ |
| #3,900 | Goekdeniz-Guelmez/mlx-lm-lora This Project | Python | ★ 419 | |
| #3,900 | gravity-ui/markdown-editor | TypeScript | ★ 419 | Compare ↗ |
| #3,900 | bobluppes/graaf | C++ | ★ 419 | Compare ↗ |
| #3,903 | turn-project/turn | Ruby | ★ 418 | Compare ↗ |
| #3,903 | kulics/koral | Swift | ★ 418 | Compare ↗ |
| #3,905 | CloudburstMC/Protocol | Java | ★ 417 | Compare ↗ |
Frequently Asked Questions
What does mlx-lm-lora do? +
Train Large Language Models on MLX.
What language is mlx-lm-lora written in? +
The primary language is Python. Topics include: apple, deep-learning, dpo, fine, finetuning-llms.
Is mlx-lm-lora actively maintained? +
Yes, the last recorded push was on Sep 28, 2026 with 2 open issues being tracked.
How many stars does mlx-lm-lora have? +
mlx-lm-lora has 419 stars and 57 forks on GitHub.
How does mlx-lm-lora rank among GitHub repositories? +
With 419 stars, Goekdeniz-Guelmez/mlx-lm-lora is ranked #3,900 globally across all repositories tracked on GitHubRepo and #463 among Python projects.
What license is mlx-lm-lora distributed under? +
The repository reports a Apache-2.0 license. Always verify the repository LICENSE file for legal terms.
